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基于机器学习的框架在多中心队列中为肾癌开发肿瘤血栓形成特征。

Machine Learning-based Framework Develops a Tumor Thrombus Coagulation Signature in Multicenter Cohorts for Renal Cancer.

机构信息

Qingdao Institute, School of Life Medicine, Department of Urology, Fudan University Shanghai Cancer Center, Fudan University, Qingdao, 266500, China.

Department of Urology, State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200433, China.

出版信息

Int J Biol Sci. 2024 Jul 1;20(9):3590-3620. doi: 10.7150/ijbs.94555. eCollection 2024.

DOI:10.7150/ijbs.94555
PMID:38993563
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11234220/
Abstract

Renal cell carcinoma (RCC) is frequently accompanied by tumor thrombus in the venous system with an extremely dismal prognosis. The current Tumor Node Metastasis (TNM) stage and Mayo clinical classification do not appropriately identify preference-sensitive treatment. Therefore, there is an urgent need to develop a better ideal model for precision medicine. In this study, we developed a coagulation tumor thrombus signature for RCC with 10 machine-learning algorithms (101 combinations) based on a novel computational framework using multiple independent cohorts. The established tumor thrombus coagulation-related risk stratification (TTCRRS) signature comprises 10 prognostic coagulation-related genes (CRGs). This signature could predict survival outcomes in public and in-house protein cohorts and showed high performance compared to 129 published signatures. Additionally, the TTCRRS signature was significantly related to some immune landscapes, immunotherapy response, and chemotherapy. Furthermore, we also screened out hub genes, transcription factors, and small compounds based on the TTCRRS signature. Meanwhile, CYP51A1 can regulate the proliferation and migration properties of RCC. The TTCRRS signature can complement the traditional anatomic TNM staging system and Mayo clinical stratification and provide clinicians with more therapeutic options.

摘要

肾细胞癌（RCC）常伴有静脉系统中的肿瘤血栓，预后极差。目前的肿瘤淋巴结转移（TNM）分期和 Mayo 临床分类不能准确识别偏好敏感的治疗方法。因此，迫切需要开发一种更好的精准医学理想模型。在这项研究中，我们基于一种新的计算框架，使用多个独立队列，利用 10 种机器学习算法（101 种组合），开发了一种用于 RCC 的凝血肿瘤血栓特征。所建立的肿瘤血栓凝血相关风险分层（TTCRRS）特征包括 10 个预后相关的凝血基因（CRGs）。该特征可预测公共和内部蛋白质队列的生存结果，与 129 个已发表特征相比，表现出较高的性能。此外，TTCRRS 特征与一些免疫景观、免疫治疗反应和化疗显著相关。此外，我们还基于 TTCRRS 特征筛选出了枢纽基因、转录因子和小分子化合物。同时，CYP51A1 可以调节 RCC 的增殖和迁移特性。TTCRRS 特征可以补充传统的解剖学 TNM 分期系统和 Mayo 临床分层，为临床医生提供更多的治疗选择。

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Genes (Basel). 2022 May 10;13(5):851. doi: 10.3390/genes13050851.
3
A proteogenomic analysis of clear cell renal cell carcinoma in a Chinese population.中国人群中肾透明细胞癌的蛋白质基因组分析
Nat Commun. 2022 Apr 19;13(1):2052. doi: 10.1038/s41467-022-29577-x.
4
Decoding the multicellular ecosystem of vena caval tumor thrombus in clear cell renal cell carcinoma by single-cell RNA sequencing.通过单细胞 RNA 测序解码透明细胞肾细胞癌下腔静脉癌栓的多细胞生态系统。
Genome Biol. 2022 Mar 31;23(1):87. doi: 10.1186/s13059-022-02651-9.
5
Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer.基于机器学习的整合开发了一种免疫源性 lncRNA 特征，用于改善结直肠癌的预后。
Nat Commun. 2022 Feb 10;13(1):816. doi: 10.1038/s41467-022-28421-6.
6
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7
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8
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